Hierarchical Shrinkage Priors for Dynamic Regressions with Many Predictors

33 Pages Posted: 17 Apr 2011

See all articles by Dimitris Korobilis

Dimitris Korobilis

University of Glasgow - Adam Smith Business School

Date Written: April 17, 2011

Abstract

This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarchical Normal-Gamma priors. Various popular penalized least squares estimators for shrinkage and selection in regression models can be recovered using this single hierarchical Bayes formulation. Using 129 U.S. macroeconomic quarterly variables for the period 1959-2010 I exhaustively evaluate the forecasting properties of Bayesian shrinkage in regressions with many predictors. Results show that for particular data series hierarchical shrinkage dominates factor model forecasts, and hence it becomes a valuable addition to existing methods for handling large dimensional data.

Keywords: Forecasting, shrinkage, factor model, variable selection, Bayesian LASSO

JEL Classification: C11, C22, C52, C53, C63, E37

Suggested Citation

Korobilis, Dimitris, Hierarchical Shrinkage Priors for Dynamic Regressions with Many Predictors (April 17, 2011). Available at SSRN: https://ssrn.com/abstract=1812192 or http://dx.doi.org/10.2139/ssrn.1812192

Dimitris Korobilis (Contact Author)

University of Glasgow - Adam Smith Business School ( email )

40 University Avenue
Gilbert Scott Building
Glasgow, Scotland G12 8QQ
United Kingdom

HOME PAGE: http://https://sites.google.com/site/dimitriskorobilis/

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